Dynamic modelling, validation and analysis of coal-fired subcritical power plant

Coal-fired power plants are the main source of global electricity. As environmental regulations tighten, there is need to improve the design, operation and control of existing or new built coal-fired power plants. Modelling and simulation is identified as an economic, safe and reliable approach to reach this objective. In this study, a detailed dynamic model of a 500 MWe coal-fired subcritical power plant was developed using gPROMS based on first principles. Model validations were performed against actual plant measurements and the relative error was less than 5%. The model is able to predict plant performance reasonably from 70% load level to full load. Our analysis showed that implementing load changes through ramping introduces less process disturbances than step change. The model can be useful for providing operator training and for process troubleshooting among others

Process analysis of pressurized oxy-coal power cycle for carbon capture application integrated with liquid air power generation and binary cycle engines

In this paper, the thermodynamic advantage of integrating liquid air power generation (LAPG) process and binary cycle waste heat recovery technology to a standalone pressurized oxy-coal combustion supercritical steam power generation cycle is investigated through modeling and simulation using Aspen Plus® simulation software version 8.4. The study shows that the integration of LAPG process and the use of binary cycle heat engine which convert waste heat from compressor exhaust to electricity, in a standalone pressurized oxy-coal combustion supercritical steam power generation cycle improves the thermodynamic efficiency of the pressurized oxy-coal process. The analysis indicates that such integration can give about 12–15% increase in thermodynamic efficiency when compared with a standalone pressurized oxy-coal process with or without CO2 capture. It was also found that in a pressurized oxy-coal process, it is better to pump the liquid oxygen from the cryogenic ASU to a very high pressure prior to vapourization in the cryogenic ASU main heat exchanger and subsequently expand the gaseous oxygen to the required combustor pressure than either compressing the atmospheric gaseous oxygen produced from the cryogenic ASU directly to the combustor pressure or pumping the liquid oxygen to the combustor pressure prior to vapourization in the cryogenic ASU main heat exchanger. The power generated from the compressor heat in the flue gas purification, carbon capture and compression unit using binary cycle heat engine was also found to offset about 65% of the power consumed in the flue gas cleaning and compression process. The work presented here shows that there is a synergistic and thermodynamic advantage of utilizing the nitrogen-rich stream from the cryogenic ASU of an oxy-fuel power generation process for power generation instead of discarding it as a waste stream

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level)

Case study on CO₂ transport pipeline network design for Humber region in the UK

Reliable, safe and economic CO₂ transport from CO₂ capture points to long term storage/enhanced oil recovery (EOR) sites is critical for commercial deployment of carbon capture and storage (CCS) technology. Pipeline transportation of CO₂ is considered most feasible. However, in CCS applications there is concern about associated impurities and huge volumes of high pressure CO₂ transported over distances likely to be densely populated areas. On this basis, there is limited experience for design and economic assessment of CO₂ pipeline. The Humber region in the UK is a likely site for building CO₂ pipelines in the future due to large CO₂ emissions in the region and its close access to depleted gas fields and saline aquifers beneath the North Sea. In this paper, various issues to be considered in CO₂ pipeline design for CCS applications are discussed. Also, different techno-economic correlations for CO₂ pipelines are assessed using the Humber region as case study. Levelized cost of CO₂ pipelines calculated for the region range from 0.14 to 0.75 GBP per tonne of CO₂. This is a preliminary study and is useful for obtaining quick techno-economic assessment of CO₂ pipelines

Biodiesel from microalgae : the use of multi-criteria decision analysis for strain selection

Microalgae strain selection is a vital step in the production of biodiesel from microalgae. In this study, Multi-Criteria Decision Analysis (MCDA) methodologies are adopted to resolve this problem. The aim of this study is to identify the best microalgae strain for viable biodiesel production. The microalgae strains considered here are Heynigia sp., Scenedesmus sp., Niracticinium sp., Chlorella vulgaris, Chlorella sorokiniana and Auxenochlorella protothecoides. The five MCDA methods used to evaluate different strains of microalgae are Analytic Hierarchy Process (AHP), Weighted Sum Method (WSM), Weighted Product Method (WPM), Discrete Compromise Programming (DCP) and Technique for the Order of Preference to the Ideal Solution (TOPSIS). Pairwise comparison matrices are used to determine the weights of the evaluation criteria and it is observed that the most important evaluation criteria are lipid content and growth rate. From the results, Scenedesmus sp. is selected as the best microalgae strain among the six alternatives due to its high lipid content and relatively fast growth rate. The AHP is the most comprehensive of the five MCDA methods because it considers the importance of each criterion and inconsistencies in the rankings are verified. The implementation of the MCDA methods and the results from this study provide an idea of how MCDA can be applied in microalgae strain selection

Steady state simulation and exergy analysis of supercritical coal-fired power plant with CO₂ capture

Integrating a power plant with CO₂ capture incurs serious efficiency and energy penalty due to use of energy for solvent regeneration in the capture process. Reducing the exergy destruction and losses associated with the power plant systems can improve the rational efficiency of the system and thereby reducing energy penalties. This paper presents steady state simulation and exergy analysis of supercritical coal-fired power plant (SCPP) integrated with post-combustion CO₂ capture (PCC). The simulation was validated by comparing the results with a greenfield design case study based on a 550 MWe SCPP unit. The analyses show that the once-through boiler exhibits the highest exergy destruction but also has a limited influence on fuel-saving potentials of the system. The turbine subsystems show lower exergy destruction compared to the boiler subsystem but more significance in fuel-saving potentials of the system. Four cases of the integrated SCPP-CO2 capture configuration was considered for reducing thermodynamic irreversibilities in the system by reducing the driving forces responsible for the CO₂ capture process: conventional process, absorber intercooling (AIC), split-flow (SF), and a combination of absorber intercooling and split-flow (AIC + SF). The AIC + SF configuration shows the most significant reduction in exergy destruction when compared to the SCPP system with conventional CO₂ capture. This study shows that improvement in turbine performance design and the driving forces responsible for CO₂ capture (without compromising cost) can help improve the rational efficiency of the integrated system

Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

Techno-economic analysis of a CO₂ capture plant integrated with a commercial scale combined cycle gas turbine (CCGT) power plant

In this study, a combined cycle gas turbine (CCGT) power plant and a CO₂ capture plant have been modelled in GateCycle® and in Aspen Plus® environments respectively. The capture plant model is validated with experimental data from the pilot plant at the University of Texas at Austin and then has been scaled up to meet the requirement of the 427 MWe CCGT power plant. A techno-economical evaluation study has been performed with the capture plant model integrated with flue gas pre-processing and CO₂ compression sections. Sensitivity analysis was carried out to assess capture plant response to changes in key operating parameters and equipment design. The study indicates which parameters are the most relevant (namely absorber packing height and regenerator operating pressure) and how, with a proper choice of the operating conditions, both the energy requirement for solvent regeneration and the cost of electricity may be reduced